
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask


# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")


# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()


# profile = ProfileReport(df_tvshows)
# profile


def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()


data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation :


# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)


data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :


df_tvshows.head()


df_tvshows.describe()


df_tvshows.corr()


# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('Seasons', ascending = False)


# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows


# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]


df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]


df_tvshows_season = df_tvshows.copy()


df_tvshows_season.loc[df_tvshows_season['Seasons'] == "NA"] = 1
df_tvshows_season.drop(df_tvshows_season.loc[df_tvshows_season['Seasons'] == "NA"].index, inplace = True)
# df_tvshows_season = df_tvshows_season[df_tvshows_season.Seasons != "NA"]
df_tvshows_season['Seasons'] = df_tvshows_season['Seasons'].astype(int)


# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_season_tvshows = df_tvshows_season.loc[df_tvshows_season['Netflix'] == 1]
hulu_season_tvshows = df_tvshows_season.loc[df_tvshows_season['Hulu'] == 1]
prime_video_season_tvshows = df_tvshows_season.loc[df_tvshows_season['Prime Video'] == 1]
disney_season_tvshows = df_tvshows_season.loc[df_tvshows_season['Disney+'] == 1]


df_tvshows_season_group = df_tvshows_season.copy()


plt.figure(figsize = (10, 10))
corr = df_tvshows_season.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>


df_season_high_tvshows = df_tvshows_season.sort_values(by = 'Seasons', ascending = False).reset_index()
df_season_high_tvshows = df_season_high_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_season['Seasons'] == (df_tvshows_season['Seasons'].max()))
# df_season_high_tvshows = df_tvshows_season[filter]
 
# highest_rated_tvshows = df_tvshows_season.loc[df_tvshows_season['Seasons'].idxmax()]
 
print('\nTV Shows with Highest Ever Seasons  are : \n')
df_season_high_tvshows.head(5)

TV Shows with Highest Ever Seasons  are :


fig = px.bar(y = df_season_high_tvshows['Title'][:15],
             x = df_season_high_tvshows['Seasons'][:15], 
             color = df_season_high_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Highest Seasons in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_season_low_tvshows = df_tvshows_season.sort_values(by = 'Seasons', ascending = True).reset_index()
df_season_low_tvshows = df_season_low_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_season['Seasons'] == (df_tvshows_season['Seasons'].min()))
# df_season_low_tvshows = df_tvshows_season[filter]

print('\nTV Shows with Lowest Ever Seasons  are : \n')
df_season_low_tvshows.head(5)

TV Shows with Lowest Ever Seasons  are :


fig = px.bar(y = df_season_low_tvshows['Title'][:15],
             x = df_season_low_tvshows['Seasons'][:15], 
             color = df_season_low_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Lowest Seasons in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_tvshows_season['Seasons'].unique().shape[0]}' unique Seasons s were Given, They were Like this,\n
      
{df_tvshows_season.sort_values(by = 'Seasons', ascending = False)['Seasons'].unique()}\n
 
      The Highest Ever Seasons Ever Any TV Show Got is '{df_season_high_tvshows['Title'][0]}' : '{df_season_high_tvshows['Seasons'].max()}'\n
 
      The Lowest Ever Seasons Ever Any TV Show Got is '{df_season_low_tvshows['Title'][0]}' : '{df_season_low_tvshows['Seasons'].min()}'\n
      ''')

      Total '48' unique Seasons s were Given, They were Like this,

      
[53 51 49 48 46 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25
 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3  2  1]

 
      The Highest Ever Seasons Ever Any TV Show Got is 'Case Closed' : '53'

 
      The Lowest Ever Seasons Ever Any TV Show Got is 'United States of Animals' : '1'


netflix_season_high_tvshows = df_season_high_tvshows.loc[df_season_high_tvshows['Netflix']==1].reset_index()
netflix_season_high_tvshows = netflix_season_high_tvshows.drop(['index'], axis = 1)
 
netflix_season_low_tvshows = df_season_low_tvshows.loc[df_season_low_tvshows['Netflix']==1].reset_index()
netflix_season_low_tvshows = netflix_season_low_tvshows.drop(['index'], axis = 1)
 
netflix_season_high_tvshows.head(5)


fig = px.bar(y = netflix_season_high_tvshows['Title'][:15],
             x = netflix_season_high_tvshows['Seasons'][:15], 
             color = netflix_season_high_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Highest Seasons in Minutes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = netflix_season_low_tvshows['Title'][:15],
             x = netflix_season_low_tvshows['Seasons'][:15], 
             color = netflix_season_low_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Lowest Seasons in Minutes : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


hulu_season_high_tvshows = df_season_high_tvshows.loc[df_season_high_tvshows['Hulu']==1].reset_index()
hulu_season_high_tvshows = hulu_season_high_tvshows.drop(['index'], axis = 1)
 
hulu_season_low_tvshows = df_season_low_tvshows.loc[df_season_low_tvshows['Hulu']==1].reset_index()
hulu_season_low_tvshows = hulu_season_low_tvshows.drop(['index'], axis = 1)
 
hulu_season_high_tvshows.head(5)


fig = px.bar(y = hulu_season_high_tvshows['Title'][:15],
             x = hulu_season_high_tvshows['Seasons'][:15], 
             color = hulu_season_high_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Highest Seasons in Minutes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = hulu_season_low_tvshows['Title'][:15],
             x = hulu_season_low_tvshows['Seasons'][:15], 
             color = hulu_season_low_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Lowest Seasons in Minutes : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


prime_video_season_high_tvshows = df_season_high_tvshows.loc[df_season_high_tvshows['Prime Video']==1].reset_index()
prime_video_season_high_tvshows = prime_video_season_high_tvshows.drop(['index'], axis = 1)
 
prime_video_season_low_tvshows = df_season_low_tvshows.loc[df_season_low_tvshows['Prime Video']==1].reset_index()
prime_video_season_low_tvshows = prime_video_season_low_tvshows.drop(['index'], axis = 1)
 
prime_video_season_high_tvshows.head(5)


fig = px.bar(y = prime_video_season_high_tvshows['Title'][:15],
             x = prime_video_season_high_tvshows['Seasons'][:15], 
             color = prime_video_season_high_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Highest Seasons in Minutes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = prime_video_season_low_tvshows['Title'][:15],
             x = prime_video_season_low_tvshows['Seasons'][:15], 
             color = prime_video_season_low_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Lowest Seasons in Minutes : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


disney_season_high_tvshows = df_season_high_tvshows.loc[df_season_high_tvshows['Disney+']==1].reset_index()
disney_season_high_tvshows = disney_season_high_tvshows.drop(['index'], axis = 1)
 
disney_season_low_tvshows = df_season_low_tvshows.loc[df_season_low_tvshows['Disney+']==1].reset_index()
disney_season_low_tvshows = disney_season_low_tvshows.drop(['index'], axis = 1)
 
disney_season_high_tvshows.head(5)


fig = px.bar(y = disney_season_high_tvshows['Title'][:15],
             x = disney_season_high_tvshows['Seasons'][:15], 
             color = disney_season_high_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Highest Seasons in Minutes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = disney_season_low_tvshows['Title'][:15],
             x = disney_season_low_tvshows['Seasons'][:15], 
             color = disney_season_low_tvshows['Seasons'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Lowest Seasons in Minutes : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      The TV Show with Highest Seasons  Ever Got is '{df_season_high_tvshows['Title'][0]}' : '{df_season_high_tvshows['Seasons'].max()}'\n
      The TV Show with Lowest Seasons  Ever Got is '{df_season_low_tvshows['Title'][0]}' : '{df_season_low_tvshows['Seasons'].min()}'\n
      
      The TV Show with Highest Seasons  on 'Netflix' is '{netflix_season_high_tvshows['Title'][0]}' : '{netflix_season_high_tvshows['Seasons'].max()}'\n
      The TV Show with Lowest Seasons  on 'Netflix' is '{netflix_season_low_tvshows['Title'][0]}' : '{netflix_season_low_tvshows['Seasons'].min()}'\n
      
      The TV Show with Highest Seasons  on 'Hulu' is '{hulu_season_high_tvshows['Title'][0]}' : '{hulu_season_high_tvshows['Seasons'].max()}'\n
      The TV Show with Lowest Seasons  on 'Hulu' is '{hulu_season_low_tvshows['Title'][0]}' : '{hulu_season_low_tvshows['Seasons'].min()}'\n
      
      The TV Show with Highest Seasons  on 'Prime Video' is '{prime_video_season_high_tvshows['Title'][0]}' : '{prime_video_season_high_tvshows['Seasons'].max()}'\n
      The TV Show with Lowest Seasons  on 'Prime Video' is '{prime_video_season_low_tvshows['Title'][0]}' : '{prime_video_season_low_tvshows['Seasons'].min()}'\n
      
      The TV Show with Highest Seasons  on 'Disney+' is '{disney_season_high_tvshows['Title'][0]}' : '{disney_season_high_tvshows['Seasons'].max()}'\n
      The TV Show with Lowest Seasons  on 'Disney+' is '{disney_season_low_tvshows['Title'][0]}' : '{disney_season_low_tvshows['Seasons'].min()}'\n 
      ''')

      The TV Show with Highest Seasons  Ever Got is 'Case Closed' : '53'

      The TV Show with Lowest Seasons  Ever Got is 'United States of Animals' : '1'

      
      The TV Show with Highest Seasons  on 'Netflix' is 'Case Closed' : '53'

      The TV Show with Lowest Seasons  on 'Netflix' is '1' : '1'

      
      The TV Show with Highest Seasons  on 'Hulu' is 'Case Closed' : '53'

      The TV Show with Lowest Seasons  on 'Hulu' is '1' : '1'

      
      The TV Show with Highest Seasons  on 'Prime Video' is 'Sesame Street' : '51'

      The TV Show with Lowest Seasons  on 'Prime Video' is '1' : '1'

      
      The TV Show with Highest Seasons  on 'Disney+' is 'The Simpsons' : '34'

      The TV Show with Lowest Seasons  on 'Disney+' is 'United States of Animals' : '1'


print(f'''
      Accross All Platforms the Average Seasons  is '{round(df_tvshows_season['Seasons'].mean(), ndigits = 2)}'\n
      The Average Seasons  on 'Netflix' is '{round(netflix_season_tvshows['Seasons'].mean(), ndigits = 2)}'\n
      The Average Seasons  on 'Hulu' is '{round(hulu_season_tvshows['Seasons'].mean(), ndigits = 2)}'\n
      The Average Seasons  on 'Prime Video' is '{round(prime_video_season_tvshows['Seasons'].mean(), ndigits = 2)}'\n
      The Average Seasons  on 'Disney+' is '{round(disney_season_tvshows['Seasons'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Seasons  is '3.01'

      The Average Seasons  on 'Netflix' is '2.3'

      The Average Seasons  on 'Hulu' is '3.54'

      The Average Seasons  on 'Prime Video' is '2.56'

      The Average Seasons  on 'Disney+' is '1.45'


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_season['Seasons'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_season['Seasons'], ax = ax[1])
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Seasons s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_season_tvshows['Seasons'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_season_tvshows['Seasons'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_season_tvshows['Seasons'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_season_tvshows['Seasons'][:100], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


def round_val(data):
    if str(data) != 'nan':
        return round(data)


df_tvshows_season_group['Seasons Group'] = df_tvshows_season['Seasons'].apply(round_val)
 
season_values = df_tvshows_season_group['Seasons Group'].value_counts().sort_index(ascending = False).tolist()
season_index = df_tvshows_season_group['Seasons Group'].value_counts().sort_index(ascending = False).index
 
# season_values, season_index


season_group_count = df_tvshows_season_group.groupby('Seasons Group')['Title'].count()
season_group_tvshows = df_tvshows_season_group.groupby('Seasons Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
season_group_data_tvshows = pd.concat([season_group_count, season_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
season_group_data_tvshows = season_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)


# Seasons Group with TV Shows Counts - All Platforms Combined
season_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)


season_group_data_tvshows.sort_values(by = 'Seasons Group', ascending = False)


fig = px.bar(y = season_group_data_tvshows['TV Shows Count'],
             x = season_group_data_tvshows['Seasons Group'], 
             color = season_group_data_tvshows['Seasons Group'],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows Count', 'x' : 'Seasons : In Minutes'},
             title  = 'TV Shows with Group Seasons in Minutes : All Platforms')

fig.update_layout(plot_bgcolor = "white")
fig.show()


fig = px.pie(season_group_data_tvshows[:10],
             names = season_group_data_tvshows['Seasons Group'][:10],
             values = season_group_data_tvshows['TV Shows Count'][:10],
             color = season_group_data_tvshows['TV Shows Count'][:10],
             color_discrete_sequence = px.colors.sequential.Teal)

fig.update_traces(textinfo = 'percent+label',
                  title = 'TV Shows Count based on Seasons Group')
fig.show()


df_season_group_high_tvshows = season_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_season_group_high_tvshows = df_season_group_high_tvshows.drop(['index'], axis = 1)
# filter = (season_group_data_tvshows['TV Shows Count'] ==  (season_group_data_tvshows['TV Shows Count'].max()))
# df_season_group_high_tvshows = season_group_data_tvshows[filter]
 
# highest_rated_tvshows = season_group_data_tvshows.loc[season_group_data_tvshows['TV Shows Count'].idxmax()]
 
# print('\nSeasons with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_season_group_high_tvshows.head(5)


df_season_group_low_tvshows = season_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_season_group_low_tvshows = df_season_group_low_tvshows.drop(['index'], axis = 1)
# filter = (season_group_data_tvshows['TV Shows Count'] = =  (season_group_data_tvshows['TV Shows Count'].min()))
# df_season_group_low_tvshows = season_group_data_tvshows[filter]
 
# print('\nSeasons with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_season_group_low_tvshows.head(5)


print(f'''
      Total '{df_tvshows_season['Seasons'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{season_group_data_tvshows['Seasons Group'].unique().shape[0]}' Seasons Group, They were Like this, \n
 
      {season_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Seasons Group'].unique()} etc. \n
 
      The Seasons Group with Highest TV Shows Count have '{season_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_season_group_high_tvshows['Seasons Group'][0]}', &\n
      The Seasons Group with Lowest TV Shows Count have '{season_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_season_group_low_tvshows['Seasons Group'][0]}'
      ''')

      Total '5432' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '48' Seasons Group, They were Like this, 

 
      [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 18 16 21 17 20 19 22 25 24
 27 31 30 23 51 32 33 26 39 28 29 34 35 36 38 43 37 41 42 46 48 49 40 53] etc. 

 
      The Seasons Group with Highest TV Shows Count have '2720' TV Shows Available is '1', &

      The Seasons Group with Lowest TV Shows Count have '1' TV Shows Available is '53'


netflix_season_group_tvshows = season_group_data_tvshows[season_group_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_season_group_tvshows = netflix_season_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_season_group_high_tvshows = df_season_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_season_group_high_tvshows = netflix_season_group_high_tvshows.drop(['index'], axis = 1)
 
netflix_season_group_low_tvshows = df_season_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_season_group_low_tvshows = netflix_season_group_low_tvshows.drop(['index'], axis = 1)
 
netflix_season_group_high_tvshows.head(5)


hulu_season_group_tvshows = season_group_data_tvshows[season_group_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_season_group_tvshows = hulu_season_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_season_group_high_tvshows = df_season_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_season_group_high_tvshows = hulu_season_group_high_tvshows.drop(['index'], axis = 1)
 
hulu_season_group_low_tvshows = df_season_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_season_group_low_tvshows = hulu_season_group_low_tvshows.drop(['index'], axis = 1)
 
hulu_season_group_high_tvshows.head(5)


prime_video_season_group_tvshows = season_group_data_tvshows[season_group_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_season_group_tvshows = prime_video_season_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_season_group_high_tvshows = df_season_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_season_group_high_tvshows = prime_video_season_group_high_tvshows.drop(['index'], axis = 1)
 
prime_video_season_group_low_tvshows = df_season_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_season_group_low_tvshows = prime_video_season_group_low_tvshows.drop(['index'], axis = 1)
 
prime_video_season_group_high_tvshows.head(5)


disney_season_group_tvshows = season_group_data_tvshows[season_group_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_season_group_tvshows = disney_season_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_season_group_high_tvshows = df_season_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_season_group_high_tvshows = disney_season_group_high_tvshows.drop(['index'], axis = 1)
 
disney_season_group_low_tvshows = df_season_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_season_group_low_tvshows = disney_season_group_low_tvshows.drop(['index'], axis = 1)
 
disney_season_group_high_tvshows.head(5)


print(f'''
      The Seasons Group with Highest TV Shows Count Ever Got is '{df_season_group_high_tvshows['Seasons Group'][0]}' : '{df_season_group_high_tvshows['TV Shows Count'].max()}'\n
      The Seasons Group with Lowest TV Shows Count Ever Got is '{df_season_group_low_tvshows['Seasons Group'][0]}' : '{df_season_group_low_tvshows['TV Shows Count'].min()}'\n
      
      The Seasons Group with Highest TV Shows Count on 'Netflix' is '{netflix_season_group_high_tvshows['Seasons Group'][0]}' : '{netflix_season_group_high_tvshows['Netflix'].max()}'\n
      The Seasons Group with Lowest TV Shows Count on 'Netflix' is '{netflix_season_group_low_tvshows['Seasons Group'][0]}' : '{netflix_season_group_low_tvshows['Netflix'].min()}'\n
      
      The Seasons Group with Highest TV Shows Count on 'Hulu' is '{hulu_season_group_high_tvshows['Seasons Group'][0]}' : '{hulu_season_group_high_tvshows['Hulu'].max()}'\n
      The Seasons Group with Lowest TV Shows Count on 'Hulu' is '{hulu_season_group_low_tvshows['Seasons Group'][0]}' : '{hulu_season_group_low_tvshows['Hulu'].min()}'\n
      
      The Seasons Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_season_group_high_tvshows['Seasons Group'][0]}' : '{prime_video_season_group_high_tvshows['Prime Video'].max()}'\n
      The Seasons Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_season_group_low_tvshows['Seasons Group'][0]}' : '{prime_video_season_group_low_tvshows['Prime Video'].min()}'\n
      
      The Seasons Group with Highest TV Shows Count on 'Disney+' is '{disney_season_group_high_tvshows['Seasons Group'][0]}' : '{disney_season_group_high_tvshows['Disney+'].max()}'\n
      The Seasons Group with Lowest TV Shows Count on 'Disney+' is '{disney_season_group_low_tvshows['Seasons Group'][0]}' : '{disney_season_group_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Seasons Group with Highest TV Shows Count Ever Got is '1' : '2720'

      The Seasons Group with Lowest TV Shows Count Ever Got is '53' : '1'

      
      The Seasons Group with Highest TV Shows Count on 'Netflix' is '1' : '1491'

      The Seasons Group with Lowest TV Shows Count on 'Netflix' is '34' : '0'

      
      The Seasons Group with Highest TV Shows Count on 'Hulu' is '1' : '1129'

      The Seasons Group with Lowest TV Shows Count on 'Hulu' is '40' : '0'

      
      The Seasons Group with Highest TV Shows Count on 'Prime Video' is '1' : '1483'

      The Seasons Group with Lowest TV Shows Count on 'Prime Video' is '53' : '0'

      
      The Seasons Group with Highest TV Shows Count on 'Disney+' is '1' : '728'

      The Seasons Group with Lowest TV Shows Count on 'Disney+' is '24' : '0'


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.barplot(x = netflix_season_group_tvshows['Seasons Group'][:10], y = netflix_season_group_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = hulu_season_group_tvshows['Seasons Group'][:10], y = hulu_season_group_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = prime_video_season_group_tvshows['Seasons Group'][:10], y = prime_video_season_group_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = disney_season_group_tvshows['Seasons Group'][:10], y = disney_season_group_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
 
plt.show()


plt.figure(figsize = (20, 5))
sns.lineplot(x = season_group_data_tvshows['Seasons Group'], y = season_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = season_group_data_tvshows['Seasons Group'], y = season_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = season_group_data_tvshows['Seasons Group'], y = season_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = season_group_data_tvshows['Seasons Group'], y = season_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Seasons Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()


print(f'''
      Accross All Platforms Total Count of Seasons Group is '{season_group_data_tvshows['Seasons Group'].unique().shape[0]}'\n
      Total Count of Seasons Group on 'Netflix' is '{netflix_season_group_tvshows['Seasons Group'].unique().shape[0]}'\n
      Total Count of Seasons Group on 'Hulu' is '{hulu_season_group_tvshows['Seasons Group'].unique().shape[0]}'\n
      Total Count of Seasons Group on 'Prime Video' is '{prime_video_season_group_tvshows['Seasons Group'].unique().shape[0]}'\n
      Total Count of Seasons Group on 'Disney+' is '{disney_season_group_tvshows['Seasons Group'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Seasons Group is '48'

      Total Count of Seasons Group on 'Netflix' is '30'

      Total Count of Seasons Group on 'Hulu' is '43'

      Total Count of Seasons Group on 'Prime Video' is '32'

      Total Count of Seasons Group on 'Disney+' is '14'


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.lineplot(y = season_group_data_tvshows['Seasons Group'], x = season_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_ru_ax2 = sns.lineplot(y = season_group_data_tvshows['Seasons Group'], x = season_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_ru_ax3 = sns.lineplot(y = season_group_data_tvshows['Seasons Group'], x = season_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_ru_ax4 = sns.lineplot(y = season_group_data_tvshows['Seasons Group'], x = season_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])

plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_ru_ax1 = sns.barplot(x = season_group_data_tvshows['Seasons Group'][:10], y = season_group_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_ru_ax2 = sns.barplot(x = season_group_data_tvshows['Seasons Group'][:10], y = season_group_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_ru_ax3 = sns.barplot(x = season_group_data_tvshows['Seasons Group'][:10], y = season_group_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_ru_ax4 = sns.barplot(x = season_group_data_tvshows['Seasons Group'][:10], y = season_group_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_ru_ax1.title.set_text(labels[0])
h_ru_ax2.title.set_text(labels[1])
p_ru_ax3.title.set_text(labels[2])
d_ru_ax4.title.set_text(labels[3])
 
plt.show()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	1

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Type	Service Provider
0	723	Case Closed	1996	7	8.4	NA	NA	Minami Takayama,Wakana Yamazaki,Hoang Khuyet,A...	Animation,Action,Adventure,Comedy,Crime,Drama,...	Japan,Italy,United Kingdom,Mexico,Spain	...	NA	25	tv series	53	1	1	0	1	Netflix
1	2481	Sesame Street	1969	0	8.1	NA	NA	Caroll Spinney,Frank Oz,Jerry Nelson,Sonia Man...	Animation,Adventure,Comedy,Family,Fantasy,Music	United States	...	NA	55	tv series	51	0	1	1	1	Prime Video
2	2142	Bottersnikes & Gumbles	2016	7	5.2	NA	NA	Ray Chase,Robbie Daymond,Tara Sands,Alex Babic...	Animation,Adventure,Comedy,Family	United States,United Kingdom,Australia	...	When unemployed dockworker Joey Coyle finds $1...	11	tv series	51	1	0	0	1	Netflix
3	2747	Chopped	2009	0	7.5	NA	NA	Ted Allen,Amanda Freitag,Alex Guarnaschelli,Ma...	Reality-TV	United States	...	Follows James Isaac "Jimmy" Neutron (Debi Derr...	60	tv series	49	0	1	0	1	Hulu
4	869	NOVA	1974	16	8.7	NA	NA	Jay O. Sanders,Craig Sechler,Lance Lewman,Will...	Documentary	United States	...	A police investigator named Arnar travels from...	60	tv series	48	1	0	1	1	Netflix

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	5432	United States of Animals	2016	13	6.3	NA	NA	Quincy Dunn-Baker,Jo Mendenhall,Dale Anderson,...	NA	United States	...	NA	22	tv series	1	0	0	0	1	1	Disney+
1	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
2	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
3	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
4	2208	Wild Alaska	2014	NR	7.9	NA	NA	Denny Heide,Harvey Kitka,Dougray Scott	Documentary	United Kingdom	...	NA	NA	tv series	1	1	0	0	0	1	Netflix

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Disney+	Type	Service Provider
0	2262	The Simpsons	1989	16	8.6	85	NA	Dan Castellaneta,Nancy Cartwright,Harry Sheare...	Animation,Comedy	United States	...	The curious, adventure-seeking, fourth grade g...	22	tv series	34	0	1	1	1	Disney+
1	996	America's Funniest Home Videos	1989	0	6.2	NA	NA	Jess Harnell,Tom Bergeron,Bob Saget,Ernie Ande...	Comedy,Family,Reality-TV	United States	...	NA	30	tv series	31	1	1	1	1	Netflix
2	5338	Little Einsteins	2005	0	6.2	NA	NA	David Jason,Aiden Pompey,Jesse Schwartz,Natali...	Animation,Adventure,Comedy,Family,Music,Musica...	United States	...	The adventures of best friends CeCe Jones and ...	NA	tv series	20	0	0	1	1	Disney+
3	2768	The Incredible Dr. Pol	2011	7	8.6	NA	NA	Jan Pol,Ari Rubin,Diane Pol,Charles Pol,Brenda...	Documentary,Reality-TV	United States	...	Allen Walker, a young Exorcist with a cursed e...	44	tv series	18	0	1	1	1	Disney+
4	2715	Life Below Zero	2013	7	8	NA	NA	James Franzo,Sue Aikens,Agnes Hailstone,Chip H...	Documentary,Adventure,Reality-TV	United States	...	Bobby Donnell is the head of a struggling Bost...	44	tv series	14	0	1	1	1	Disney+

	Seasons Group	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	1	2720	1491	1129	1483	728
1	2	849	338	252	268	40
2	3	542	204	159	188	34
3	4	364	131	129	114	20
4	5	243	92	96	84	5
5	6	172	45	81	72	5
6	7	119	41	66	41	2
7	8	97	29	49	28	1
8	9	67	14	33	22	4
9	10	39	8	23	14	0
10	11	37	9	24	12	0
11	12	27	4	15	16	0
12	13	22	3	15	9	0
13	14	17	2	12	5	1
14	15	17	6	10	3	0
17	18	15	3	8	4	1
15	16	8	4	5	2	0
20	21	8	2	4	2	0
16	17	7	2	7	0	0
19	20	7	1	2	4	1
18	19	6	0	5	2	0
21	22	5	1	4	0	0
24	25	4	1	3	2	0
23	24	4	1	4	2	0
26	27	4	1	3	0	0
30	31	3	1	2	2	1
29	30	3	0	3	0	0
22	23	2	1	1	2	0
46	51	2	1	1	1	0
31	32	2	0	2	1	0
32	33	2	0	0	2	0
25	26	2	0	2	0	0
38	39	1	0	0	1	0
27	28	1	0	1	0	0
28	29	1	0	1	0	0
33	34	1	0	1	0	1
34	35	1	1	0	0	0
35	36	1	0	1	0	0
37	38	1	0	1	0	0
42	43	1	0	1	0	0
36	37	1	0	1	0	0
40	41	1	0	1	1	0
41	42	1	0	1	1	0
43	46	1	0	1	0	0
44	48	1	1	0	1	0
45	49	1	0	1	0	0
39	40	1	0	0	1	0
47	53	1	1	1	0	0